Dynamic content allocation for cloud-assisted service of periodic workloads

Motivated by improved models for content workload prediction, in this paper we consider the problem of dynamic content allocation for a hybrid content delivery system that combines cloud-based storage with low cost dedicated servers that have limited storage and unmetered upload bandwidth. We formulate the problem of allocating contents to the dedicated storage as a finite horizon dynamic decision problem, and show that a discrete time decision problem is a good approximation for piecewise stationary workloads. We provide an exact solution to the discrete time decision problem in the form of a mixed integer linear programming problem, propose computationally feasible approximations, and give bounds on their approximation ratios. Finally, we evaluate the algorithms using synthetic and measured traces from a commercial music on-demand service and give insight into their performance as a function of the workload characteristics.